Local null space pursuit for real-time moving object detection in aerial surveillance

Abstract

Recently, accurate detection of moving objects has achieved via principal component pursuit (PCP). However, in the case of aerial imagery, existing PCP-based detection methods suffer from low accuracy and/or high computational loads. This paper presents a novel S-PCP method, called local null space pursuit (LNSP), which achieves a high detection accuracy and real-time performance on aerial images. LNSP models the background as a subspace that lies in a low-dimensional subspace, while the moving objects are modelled as sparse. Based on these two models, LNSP proposes a new formulation for the detection problem by using multiple local null spaces and $$\ell _1$$-norm. The performance of LNSP is evaluated on challenging aerial datasets and then compared the results with relevant current state-of-the-art methods.